System and method for proactive repair of sub optimal operation of a machine

ABSTRACT

A system and computer-implemented method for identifying and repairing suboptimal operation of a machine, the computer-implemented method including: monitoring sensory input data related to an industrial machine; analyzing, using an unsupervised machine learning model, the monitored sensory inputs, wherein the output of the unsupervised machine learning model includes at least one indicator; identifying, based on the at least one indicator, at least one behavioral pattern related to the industrial machine, wherein each of the at least one behavioral pattern is indicative of at least one suboptimal operation of the industrial machine; selecting at least one corrective action based on the at least one behavioral pattern; and performing the at least one corrective action on the industrial machine.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.62/771,600 filed on Nov. 27, 2018, the contents of which are herebyincorporated by reference.

TECHNICAL FIELD

The present disclosure relates generally to maintenance systems formachines, and more specifically to monitoring machine operations andproactively repairing failures and suboptimal operations of machines.

BACKGROUND

Communications, processing, cloud computing, artificial intelligence,and other computerized technologies have advanced significantly inrecent years, heralding in new fields of technology and production.Notwithstanding these improvements, many of the industrial technologiesemployed since or before the 1970s remain in use today. Existingsolutions related to these industrial technologies have typically seenminor improvements, thereby increasing production and yield onlyslightly.

In modern manufacturing practices, manufacturers must often meet strictproduction timelines and provide flawless or nearly flawless productionquality. As a result, these manufacturers risk heavy losses whenever anunexpected machine failure occurs. A machine failure is an event thatoccurs, when a machine deviates from correct service. Errors, which aretypically deviations from the correct state of the machine, are notnecessarily failures, but may lead to and indicate potential futurefailures. Aside from failures, errors may otherwise cause unusualmachine behavior that may affect performance.

The average failure-based machine downtime for typical manufacturers(i.e., the average amount of time in which production shuts down, eitherin part or in whole, due to machine failure) is 17 days per year, i.e.,17 days of lost production and, hence revenue. In the case of a typical450-megawatt power turbine, for example, a single day of downtime cancost a manufacturer over $3 million US in lost revenue. Such downtimemay incur additional costs related to repair, safety precautions, andthe like.

In energy power plants, billions of US dollars are spent annually onensuring reliability. Specifically, billions of dollars are spent onbackup systems and redundancies to minimize production downtimes.Additionally, monitoring systems may be used to identify failuresquickly, thereby speeding up a return to production when downtimeoccurs. However, existing monitoring systems typically identify failuresonly after, during, or immediately before downtime begins.

Further, existing solutions for monitoring machine failures typicallyrely on a set of predetermined rules for each machine. These rule setsdo not account for all data that may be collected with respect to themachine, and may only be used for checking particular key parameterswhile ignoring the rest. Moreover, these rule sets are configured inadvance by engineers or other human analysts. As a result, only some ofthe collected data may be actually used by existing solutions, therebyresulting in wasted use of computing resources related to transmission,storage, and processing of unused data. Further, failure to consider allrelevant data may result in missed or otherwise inaccurate determinationof failures.

Additionally, existing solutions often rely on periodic testing atpredetermined intervals. Thus, even existing solutions that can predictfailures in advance typically return requests to perform machinemaintenance even when the machine is not in immediate danger of failing.Such premature replacement results in wasted materials and expensesspent replacing parts that are still functioning properly. Further, suchexisting solutions often determine failures only after a failure occurs.As a result, such failures may not be prevented, resulting in downtimeand lost revenue.

Furthermore, existing monitoring and maintenance solutions often requirededicated testing equipment. Consequently, these solutions typicallyrequire specialized operators who are well-trained in the operation ofeach monitoring and maintenance system. Requiring specialized operatorscan be inconvenient and costly and may introduce potential sources ofhuman error. Given the sheer amount of data that may be collected forany given machine in addition to minute fluctuations in data, a humananalyst is often not capable of adequately determining upcomingfailures.

Lastly, existing solutions present techniques by which anomalies areidentified and corrective solution recommendations are generated andprovided. The corrective solution recommendations are usually sent to auser device associated with a user responsible for maintaining themachine. Therefore, although the process of identifying a suboptimaloperation of a machine may be performed automatically, repairing anidentified suboptimal operation of a machine is still performedmanually. This is inefficient, time-consuming, and labor-intensive.

Additionally, selecting the most suitable corrective action, from avariety of possible actions, can be a challenge. Basing such a decisionon an identified pattern that may indicate an upcoming machine failureleaves no place for human error, saving precious time when decidingwhich solution would be best for a particular pattern that is associatedwith a particular failure. Currently, even when such predictions ofupcoming machine failures are available, there is no solution thatsuggests selecting a corrective solution based on a specific identifiedpattern.

It would therefore be advantageous to provide a solution that wouldovercome the deficiencies noted above.

SUMMARY

A summary of several example embodiments of the disclosure follows. Thissummary is provided for the convenience of the reader to provide a basicunderstanding of such embodiments and does not wholly define the breadthof the disclosure. This summary is not an extensive overview of allcontemplated embodiments, and is intended to neither identify key orcritical elements of all embodiments nor to delineate the scope of anyor all aspects. Its sole purpose is to present some concepts of one ormore embodiments in a simplified form as a prelude to the more detaileddescription that is presented later. For convenience, the term “certainembodiments” may be used herein to refer to a single embodiment ormultiple embodiments of the disclosure.

Certain embodiments disclosed herein include a computer-implementedmethod for identifying and repairing suboptimal operation of a machine,the computer-implemented method including: monitoring sensory input datarelated to an industrial machine; analyzing, using an unsupervisedmachine learning model, the monitored sensory inputs, wherein the outputof the unsupervised machine learning model includes at least oneindicator; identifying, based on the at least one indicator, at leastone behavioral pattern related to the industrial machine, wherein eachof the at least one behavioral pattern is indicative of at least onesuboptimal operation of the industrial machine; selecting at least onecorrective action based on the at least one behavioral pattern; andperforming the at least one corrective action on the industrial machine.

Certain embodiments disclosed herein also include a non-transitorycomputer readable medium having stored thereon instructions for causinga processing circuitry to perform a process, the process including:monitoring sensory input data related to an industrial machine;analyzing, using an unsupervised machine learning model, the monitoredsensory inputs, wherein the output of the unsupervised machine learningmodel includes at least one indicator; identifying, based on the atleast one indicator, at least one behavioral pattern related to theindustrial machine, wherein each of the at least one behavioral patternis indicative of at least one suboptimal operation of the industrialmachine; selecting at least one corrective action based on the at leastone behavioral pattern; and performing the at least one correctiveaction on the industrial machine.

Certain embodiments disclosed herein also include a system foridentifying and repairing suboptimal operation of a machine, including:a processing circuitry; and a memory, the memory containing instructionsthat, when executed by the processing circuitry, configure the systemto: monitor sensory input data related to an industrial machine;analyze, using an unsupervised machine learning model, the monitoredsensory inputs, wherein the output of the unsupervised machine learningmodel includes at least one indicator; identify, based on the at leastone indicator, at least one behavioral pattern related to the industrialmachine, wherein each of the at least one behavioral pattern isindicative of at least one suboptimal operation of the industrialmachine; select at least one corrective action based on the at least onebehavioral pattern; and perform the at least one corrective action onthe industrial machine.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter disclosed herein is particularly pointed out anddistinctly claimed in the claims at the conclusion of the specification.The foregoing and other objects, features, and advantages of thedisclosed embodiments will be apparent from the following detaileddescription taken in conjunction with the accompanying drawings.

FIG. 1 is a network diagram utilized to describe the various disclosedembodiments.

FIG. 2 is a block diagram of a management server according to anembodiment.

FIG. 3A simulates representation of a first pattern of normal sensoryinput behavior according to an embodiment.

FIG. 3B simulates representation of a second pattern of an anomaloussensory input behavior according to an embodiment.

FIG. 4 is an example flowchart illustrating a method for identifying andproactively repairing a suboptimal operation of a machine according toan embodiment.

FIG. 5 is an example flowchart illustrating a method for selecting acorrective action for execution according to an embodiment.

DETAILED DESCRIPTION

It is important to note that the embodiments disclosed herein are onlyexamples of the many advantageous uses of the innovative teachingsherein. In general, statements made in the specification of the presentapplication do not necessarily limit any of the various claimedembodiments. Moreover, some statements may apply to some inventivefeatures but not to others. In general, unless otherwise indicated,singular elements may be in plural and vice versa with no loss ofgenerality. In the drawings, like numerals refer to like parts throughseveral views.

By monitoring and analyzing sensory input data related to at least onemachine, one or more patterns that may be indicative of a suboptimaloperation of a machine, e.g., an industrial machine, are identified. Thedisclosed method further allows to automatically and proactively selectand perform corrective actions based on the identified pattern. Thecorrective actions may include tuning the machine software parameters,i.e., configuration, change machine process conditions (temperature,humidity, and the like), inventory scheduling (order boiler/pumpreplacement a week before failure, and the like).

FIG. 1 is an example network diagram 100 utilized to describe thevarious disclosed embodiments. The example network diagram 100 includesa machine monitoring system (MMS) 130, a management server 140, adatabase 150, a client device 160, and a data source 180 connectedthrough a network 110. The example network diagram 100 further includesa plurality of sensors 120-1 through 120-n (hereinafter referred toindividually as a sensor 120 and collectively as sensors 120, merely forsimplicity purposes, where n is an integer equal to or greater than 1),connected to the MMS 130. The network 110 may be, but is not limited to,a wireless, a cellular or wired network, a local area network (LAN), awide area network (WAN), a metro area network (MAN), the Internet, theworldwide web (WWW), similar networks, and any combination thereof.

The client device 160 may be, but is not limited to, a personalcomputer, a laptop, a tablet computer, a smartphone, a wearablecomputing device, or any other device capable of receiving anddisplaying notifications indicating maintenance and failure timingpredictions, corrective solution recommendations, results of supervisedanalysis, unsupervised analysis of machine operation data, or both, andthe like.

The sensors 120 are located in proximity (e.g., physical proximity) to amachine 170. The machine 170 may be any machine for which performancecan be represented via sensory data such as, but not limited to, aturbine, an engine, a welding machine, a three-dimensional (3D) printer,an injection molding machine, a combination thereof, a portion thereof,and the like. Each sensor 120 is configured to collect sensory inputdata such as, but not limited to, sound signals, ultrasound signals,light, movement tracking indicators, temperature, energy consumptionindicators, and the like based on operation of the machine 170. Thesensors 120 may include, but are not limited to, sound capturingsensors, motion tracking sensors, energy consumption meters, temperaturemeters, and the like. Any of the sensors 120 may be connectedcommunicatively or otherwise to the machine 170 (such connection is notillustrated in FIG. 1 merely for the sake of simplicity and withoutlimitation on the disclosed embodiments). It should be noted thatmultiple machines, such as the machine 170, may be connected via thenetwork 110 to the management server 140.

The data source 180 may be a server, a data warehouse, a website, acloud database, and the like. The data source 180 may be configured tostore one or more corrective solution recommendations or data associatedwith corrective actions that were utilized to solve or mitigate machinefailures that previously occurred.

The sensors 120 are connected to the MMS 130. In an embodiment, the MMS130 is configured to store and preprocess raw sensory input datareceived from the sensors 120. Alternatively, or collectively, the MMS130 may be configured to periodically retrieve collected sensory inputdata stored in, for example, the database 150. The preprocessing mayinclude, but is not limited to, data cleansing, normalization,rescaling, re-trending, reformatting, noise filtering, a combinationthereof, and the like.

The preprocessing may further include feature extraction. In anembodiment, the results of the feature extraction include features to beutilized by the management server 140 during unsupervised machinelearning in order to detect indicators. The feature extraction mayinclude, but is not limited to, dimension reduction techniques such as,but not limited to, singular value decompositions, discrete Fouriertransformations, discrete wavelet transformations, line segment methods,or a combination thereof. When such dimension reduction techniques areutilized, the preprocessing may result in a lower dimensional space forthe sensory input data. The machine monitoring system 130 is configuredto send the preprocessed sensory input data to the management server140.

In an embodiment, the management server 140 is configured to receive,through the network 110, the preprocessed sensory input data associatedwith the machine 170 from the machine monitoring system 130. The sensoryinput data may be received continuously and may be received inreal-time. In an embodiment, the management server 140 is configured tostore the sensory input data received from the machine monitoring system130. Alternatively, or collectively, the sensory input data may bestored in the database 150. The database 150 may further store sensoryinput data (raw, preprocessed, or both) collected from a plurality ofother sensors (not shown) associated with other machines (also notshown). The database 150 may further store indicators, anomalouspatterns, failure predictions, behavioral models utilized for analyzingsensory input data, or a combination thereof.

The management server 140, typically comprising at least a processingcircuitry (not shown) and a memory (not shown), the memory containstherein instructions that when executed by the processing circuitryconfigure the management server 140 as further described herein below.

In an embodiment, the management server 140 is configured to monitor thesensory input data related to at least one machine, e.g., the machine170. The monitoring process may include, for example, tracking andaggregating a plurality of parameters associated with the sensory inputdata that are related to several machine components. The monitoring maybe performed constantly and may further be performed by the machinemonitoring system 130.

In an embodiment, the management server 140 is configured to analyze thepreprocessed sensory input data. The analysis may include, but is notlimited to, unsupervised machine learning. In a further embodiment, theunsupervised machine learning may include one or more signal processingtechniques, implementation of one or more neural networks, or both. Itshould be noted that different parameters represented by the sensoryinput data may be analyzed using different machine learning techniques.For example, a temperature parameter may be analyzed by applying a firstmachine learning technique to sensory input data from a temperaturesensor, and an energy consumption parameter may be analyzed by applyinga second machine learning technique to sensory input data from an energyconsumption gauge.

In an embodiment, the management server 140 is configured toautomatically select a model that optimally indicates anomalies in thesensory input data based on, e.g., a type of one or more portions of thedata. In a further embodiment, the selection may be based on resultsfrom applying a plurality of models to each of at least a portion of thesensory input data. In yet a further embodiment, the selection may bebased further on false positive and true positive rates.

In a further embodiment, the management server 140 is configured togenerate a meta-model based on at least one portion of the machine 170.Each portion of the machine for which a meta-model is generated may be acomponent (not shown) such as, but not limited to, a pipe, an engine, aportion of an engine, a combination thereof, and the like. Generating ameta-model may include, but is not limited to, selecting a model thatoptimally indicates anomalies in the sensory input data for each of theat least one portion of the machine 170. Each of the generatedmeta-models is utilized to detect anomalies in the behavior of therespective portion of the machine 170.

In an embodiment, the management server 140 is configured to generate,in real-time, at least one adaptive threshold for detecting anomaliesbased on the analysis. In a further embodiment, the management server140 is configured to determine, in real-time or near real-time, normalmachine behavioral patterns based on the sensory input data of themachine 170 or each portion thereof.

In an embodiment, upon identifying normal machine behavioral patterns,the management server 140 is configured to identify at least one machinebehavioral pattern that is indicative of at least a suboptimal operationof the machine 170. A suboptimal operation may include a degradation ina machine production, poor machine productive, poor functioning withregard to normal functioning, machine fault, machine failure, and thelike.

It should be noted that the machine behavioral patterns may be detectedusing the at least one adaptive threshold. The adaptive thresholds maybe generated based on the determined normal behavior patterns.Generation of adaptive thresholds for detecting anomalies based onnormal behavior patterns is described further herein below with respectto FIGS. 3A and 3B. In an embodiment, the management server 140 may beconfigured to determine based on the at least one machine behavioralpattern and the monitored sensory input data, at least one machinefailure prediction. The at least one machine failure prediction may be aprediction of failure of the machine or of any portion thereof (e.g., acomponent of the machine). In an embodiment, the failures are predictedbased on similar patterns of, e.g., anomalies.

In an embodiment, based on the identified machine behavioral patternthat is indicative of a suboptimal operation of the machine, themanagement server 140 is configured to select one or more correctiveactions, e.g., from the data source 180, in order to automatically andproactively repair the suboptimal operation of the machine. In anembodiment, the proactivity of the management server 140 allows torepair and solve suboptimal operation of a machine, e.g., a machinefault, before the fault is developed into a machine failure. In afurther embodiment, the management server 140 may be configured togenerate a notification indicating anomalous activity, an upcomingmachine failure that was recognized, a machine fault, a selectedcorrective action, and so on. In a further embodiment, the managementserver 140 is further configured to send the generated notification to,e.g., the user device 160. The selection process is described further inherein below with respect of FIG. 5 .

In an embodiment, the management server 140 may be configured toproactively perform the selected at least one corrective action.Performing the selected corrective action may include for example,tuning the machine software parameters, i.e., configuration, changemachine process conditions (temperature, humidity, and the like),inventory scheduling (order boiler/pump replacement a week beforefailure, and the like).

It should be noted that the machine monitoring system 130 is shown inFIG. 1 as a separate component from the management server 140 merely forsimplicity purposes and without limitation on the disclosed embodiments.The machine monitoring system 130 may be incorporated in the managementserver 140, so as to allow the management server 140 to obtain andpreprocess sensory input data without departing from the scope of thedisclosure.

It should also be noted that the embodiments described herein above withrespect to FIG. 1 are discussed with respect to a user device 160 and amachine 170 merely for simplicity purposes and without limitation on thedisclosed embodiments. Multiple user devices may receive informationrelated to machine maintenance and failures without departing from thescope of the disclosure. Additionally, sensory input data related tomultiple machines may be collected to determine failures of any or allof the machines without departing from the scope of the disclosure.

It should be further noted that the embodiments disclosed herein are notlimited to the specific architecture illustrated in FIG. 1 and otherarchitectures may be equally used without departing from the scope ofthe disclosed embodiments. Specifically, the management server 140 mayreside in the cloud computing platform, a datacenter, on premise, andthe like. Moreover, in an embodiment, there may be a plurality ofmanagement servers operating as described hereinabove and configured toeither have one as a standby proxy to take control in a case of failure,to share the load between them, or to split the functions between them.

FIG. 2 shows an example block diagram of the management server 140implemented according to one embodiment. The management server 140includes a processing circuitry 210 coupled to a memory 220, a storage230, a network interface 240, and a machine learning (ML) engine 250. Inan embodiment, the components of the management server 140 may beconnected via a bus 260.

The processing circuitry 210 may be realized as one or more hardwarelogic components and circuits. For example, and without limitation,illustrative types of hardware logic components that can be used includefield programmable gate arrays (FPGAs), application-specific integratedcircuits (ASICs), application-specific standard products (ASSPs),system-on-a-chip systems (SOCs), general-purpose microprocessors,microcontrollers, digital signal processors (DSPs), and the like, or anyother hardware logic components that can perform calculations or othermanipulations of information.

The memory 220 may be volatile (e.g., RAM, and the like), non-volatile(e.g., ROM, flash memory, and the like), or a combination thereof. Inone configuration, computer readable instructions to implement one ormore embodiments disclosed herein may be stored in the storage 230.

In another embodiment, the memory 220 is configured to store software.Software shall be construed broadly to mean any type of instructions,whether referred to as software, firmware, middleware, microcode,hardware description language, or otherwise. Instructions may includecode (e.g., in source code format, binary code format, executable codeformat, or any other suitable format of code). The instructions, whenexecuted by the one or more processors, cause the processing circuitry210 to perform the various processes described herein. In an embodiment,the memory 220 may contain data collected by the sensors, e.g., thesensors 120 of FIG. 1 . In a further embodiment, such data may also bestored in a data warehouse such as the database.

The storage 230 may be magnetic storage, optical storage, and the like,and may be realized, for example, as flash memory or other memorytechnology, CD-ROM, Digital Versatile Disks (DVDs), or any other mediumwhich can be used to store the desired information.

The network interface 240 allows the management server 140 tocommunicate with the machine monitoring system 130 for the purpose of,for example, receiving raw and/or preprocessed sensory input data.Additionally, the network interface 240 allows the management server 140to communicate with the client device 160 in order to send, e.g.,notifications related to anomalous activity, machine suboptimaloperation, machine failure prediction, corrective solutionrecommendations, corrective actions, and the like.

The machine learning engine 250 is configured to perform machinelearning based on sensory input data received via the network interface240 as described further herein. In an embodiment, the machine learningengine 250 is further configured to determine, based on one or moremachine learning models, suboptimal operation of a machine, machinefailures, predictions for machine failures, and the like of the machine170. In a further embodiment, the machine learning engine 250 is alsoconfigured to determine at least one corrective action for repairing thesuboptimal operation of the machine. As a non-limiting example, the atleast corrective action may include tuning the machine softwareparameters, i.e., configuration, change machine process conditions(temperature, humidity, and the like), inventory scheduling (orderboiler/pump replacement a week before failure, and the like). Thelearning engine 250 may be realized as a graphics processing unit (GPU),a tensor processing unit (TPU), and the like.

It should be understood that the embodiments described herein are notlimited to the specific architecture illustrated in FIG. 2 , and otherarchitectures may be equally used without departing from the scope ofthe disclosed embodiments.

FIG. 3A is an example representation of behavioral patterns implementedaccording to an embodiment. The representation shown in FIG. 3A includesa graph 300A in which sensory input data are represented by a curve310A. In the example simulation shown in FIG. 3 , the curve 310Arepresents an aggregated behavior of the sensory input data over time.During operation of a machine (e.g., the machine 170, FIG. 1 ), theaggregated behavior represented by the curve 310A may be continuouslymonitored for repeated sequences such as repeated sequences 320A and330A. Upon determination of, for example, the repeated sequence 320A,the repeated sequence 330A, or both, a model of a normal behaviorpattern of the machine is generated.

It should be noted that continuous monitoring of two or more cycles ofbehavior may be useful for determining more accurate patterns. Asmonitoring and, consequently, learning, continue, the normal behaviormodel may be updated accordingly. The models of normal behavior patternsmay be utilized to determine machine failure predictions. As anon-limiting example, if the sequence 320A preceded a machine failure orother suboptimal operation of a machine, then the determination ofrepeated sequence 330A may be predicted to precede a machine failure orthe other suboptimal operation of a machine.

FIG. 3B is an example representation 300B illustrating generation ofadaptive thresholds. Based on one or more repeated sequences (e.g., therepeated sequences 320A and 330A), a maximum threshold 310B and aminimum threshold 320B are determined. The thresholds 310B and 320B maybe determined in real-time and regardless of past machine behavior. Inan example implementation, the thresholds 310B and 320B are dynamic andadapted based on the sequences 320A and 330A as well as any subsequentlydetermined sequences. The point 330B represents an indicator, i.e., adata point that is above the maximum threshold 310B or below the minimumthreshold 320B. Upon determination that one of the thresholds 310B or320B has been exceeded, an anomaly may be detected. In an embodiment, ananomaly is indicative of a suboptimal operation of the machine.

FIG. 4 is an example flowchart 400 illustrating a method for identifyingand proactively repairing a suboptimal operation of a machine accordingto an embodiment.

At S410, sensory input data related to at least one machine, e.g., themachine 170 of FIG. 1 , are monitored. The sensory input data mayinclude for example sound signals, ultrasound signals, light, movementtracking indicators, temperature, energy consumption indicators, and thelike based on operation of a machine, e.g., the machine 170.

At S420, the monitored sensory input data are analyzed, via at leastunsupervised machine learning. It should be noted that differentparameters represented by the sensory input data may be analyzed usingdifferent machine learning techniques. The output of the unsupervisedmachine learning may include at least one indicator. The indicators maybe associated with various features of the machine such that, forexample, abnormal temperature values of the machine may be recognizedusing temperature indicators, abnormal vibrations of the machine may berecognized using vibration indicators, and the like.

At S430, at least one machine behavioral pattern is identified based onthe at least one indicator. The machine behavioral pattern may includeone or more abnormal values associated with one or more components ofthe machine, one or more sequences of abnormal values, and the like.that are indicative of a suboptimal operation of a machine. In anembodiment, S430 may further include analyzing the identified patternby, for example, comparing the identified pattern's characteristics tohistorical patterns' characteristics of machine behavioral historicalpatterns that were previously analyzed and stored in a database. Thehistorical patterns' characteristics may be indicative of a machinefailure root cause, a relevant corrective solution, and the like. Thus,the analysis of the identified pattern allows to determine the rootcause of the machine behavioral pattern that indicates on the suboptimaloperation of the machine. The root cause may be for example, anincreasing temperature of one of the machine components, that may besolved by, for example, changing process conditions in the machine 170.

At S440, one or more corrective actions are selected based on the atleast one machine behavioral pattern. The one or more corrective actionsmay include for example, tune the machine software parameters, i.e.,configuration, change machine process conditions (temperature, humidity,and the like), inventory scheduling (order boiler/pump replacement aweek before failure, and the like). In an embodiment, the selection ofone or more corrective actions that are most likely to solve the machinefailure may be achieved based on analyzing a plurality of correctiveactions that are stored in a database and that were previouslymonitored. The analysis of the plurality of corrective actions mayinclude ranking each corrective action with respect to its potential tocontribute to repairing the machine failure. It should be noted thatmore than one corrective actions, determined to have a score that isabove a predetermined level, may be selected by the management server140. The selection of the corrective actions is further discussed withrespect of FIG. 5 .

At S450, the selected at least one corrective action is performed, i.e.executed, by the management server 140. In an embodiment, the managementserver 140 is configured to constantly monitor the influence of thecorrective action on the machine behavior. The monitoring may beachieved by analyzing the sensory input data associated with the atleast one machine, after the corrective action was performed, todetermine the influence of the executed corrective action on the atleast one machine behavior. According to another embodiment, themanagement server 140 may be configured to rank the at least onecorrective action responsive of its effectiveness for repairing thesuboptimal operation of the machine 170. The ranking may include, forexample, associating a relatively high score for a corrective actionthat fully repaired the suboptimal operation, a medium score to solvingonly part of the suboptimal operation, and a low score for a relativelypoor influence or a no influence at all on the suboptimal operation ofthe machine. According to another embodiment, the ranking may alsoinclude a negative score for corrective actions that were determined toaggravate the suboptimal operation.

According to another embodiment, the management server 140 may beconfigured to store in a data source, e.g., the data source 180, forfuture use, at least one of: the at least one machine behavioralpattern, the at least one corrective action, the ranking of the at leastone corrective action. The future use may include, for example, rankingnew corrective actions, suggest corrective actions, identify orrecognize new abnormal machine behavioral patterns, and the like.

It should be noted that in case the management server 140 determinesthat the optimal or the only corrective solution must involve manualintervention, the management server 140 may generate a correctivesolution recommendation indicating, e.g., the optimal way of action.According to another embodiment, the management server 140 may generatea corrective solution recommendation, and will not execute proactively acorrective action, even in case where the probability of damaging themachine 170 is above a predetermined threshold and no other bettercorrective actions are available. The recommendations may be sent by themanagement server 140 via the network 110 to a client device, e.g. theclient device 160 that is associated with a person who is responsiblefor the machine 170 maintenance.

FIG. 5 is an example flowchart 440 illustrating a method for selecting acorrective action for execution according to an embodiment. In anembodiment, the method may be performed by the management server 140.

At S440-10, one or more characteristics associated with the machinebehavioral pattern, that is indicative of the at least one suboptimaloperation of the machine, are extracted. The characteristics may be forexample, the type of component(s) of the machine that are associatedwith the identified machine behavioral pattern, time pointer at whichthe pattern started, pattern duration, number of anomalies identified ina pattern that indicates on the suboptimal operation of the machine, andthe like.

At S440-20, a search is performed, based on the extractedcharacteristics, in at least one data source, e.g., the data source 180,that includes a plurality of corrective actions, for one or morecorrective actions. In an embodiment, the search may be performed by themanagement server 140. Each corrective action may include metadataindicating on for example, previous cases at which the corrective actionmanaged to repair the suboptimal operation of the machine, the type ofthe suboptimal operation that was previously solved using the correctiveaction, corrective action description, and the like.

At S440-30, at least one corrective action having a first probabilityscore that is above a first predetermined threshold to repair the atleast one suboptimal operation of the machine, and a second probabilityscore that is below a second predetermined threshold to damage themachine, is selected. The first threshold may state that a score of 70%for repairing the suboptimal operation of the machine is the minimumscore a corrective action shall have in order to be a candidatecorrective action. The second threshold may state that a score of 20%for damaging the machine is the maximum score a corrective action shallhave in order to be a candidate corrective action. For example, a firstoptional corrective action having a first probability score of 97% forrepairing the machine failure with a second probability score, i.e.,risk, of 50% to cause a damage to the machine, is identified. Accordingto the same example, a second optional corrective action having a firstprobability score of only 85% for repairing the suboptimal operation ofthe machine and a second probability score of 10% to damage the machine,may be selected over the first corrective action although the firstprobability score of the first corrective action to repair the machinefailure is higher.

The various embodiments disclosed herein can be implemented as hardware,firmware, software, or any combination thereof. Moreover, the softwareis preferably implemented as an application program tangibly embodied ona program storage unit or computer readable medium consisting of parts,or of certain devices and/or a combination of devices. The applicationprogram may be uploaded to, and executed by, a machine comprising anysuitable architecture. Preferably, the machine is implemented on acomputer platform having hardware such as one or more central processingcircuitries (“CPUs”), a memory, and input/output interfaces. Thecomputer platform may also include an operating system andmicroinstruction code. The various processes and functions describedherein may be either part of the microinstruction code or part of theapplication program, or any combination thereof, which may be executedby a CPU, whether or not such a computer or processor is explicitlyshown. In addition, various other peripheral units may be connected tothe computer platform such as an additional data storage unit and aprinting unit. Furthermore, a non-transitory computer readable medium isany computer readable medium except for a transitory propagating signal.

As used herein, the phrase “at least one of” followed by a listing ofitems means that any of the listed items can be utilized individually,or any combination of two or more of the listed items can be utilized.For example, if a system is described as including “at least one of A,B, and C,” the system can include A alone; B alone; C alone; A and B incombination; B and C in combination; A and C in combination; or A, B,and C in combination.

All examples and conditional language recited herein are intended forpedagogical purposes to aid the reader in understanding the principlesof the disclosed embodiment and the concepts contributed by the inventorto furthering the art, and are to be construed as being withoutlimitation to such specifically recited examples and conditions.Moreover, all statements herein reciting principles, aspects, andembodiments of the disclosed embodiments, as well as specific examplesthereof, are intended to encompass both structural and functionalequivalents thereof. Additionally, it is intended that such equivalentsinclude both currently known equivalents as well as equivalentsdeveloped in the future, i.e., any elements developed that perform thesame function, regardless of structure.

What is claimed is:
 1. A computer-implemented method for repairingsuboptimal operation of an industrial machine, comprising: monitoringsensory input data related to an industrial machine; analyzing, using anunsupervised machine learning model, the monitored sensory inputs,wherein the output of the unsupervised machine learning model includesat least one indicator; identifying, based on the at least oneindicator, at least one behavioral pattern related to the industrialmachine, wherein each of the at least one behavioral pattern isindicative of at least one suboptimal operation of the industrialmachine; selecting at least one corrective action based on the at leastone behavioral pattern, wherein selecting at least one corrective actionfurther comprises: extracting one or more characteristics associatedwith the at least one machine behavioral pattern that is indicative ofthe at least one suboptimal operation of the at least one machine;searching in at least one data source that comprises a plurality ofcorrective actions for one or more corrective actions based on extractedcharacteristics; and, selecting at least one corrective action having afirst probability score that is above a first predetermined threshold torepair the at least one suboptimal operation of the machine, and asecond probability score that is below a second predetermined thresholdto damage the machine; and performing the at least one corrective actionon the industrial machine.
 2. The computer-implemented method of claim1, further comprising: monitoring the sensory input data associated withthe industrial machine to determine an influence of the correctiveaction on the at least one machine.
 3. The computer-implemented methodof claim 2, further comprising: ranking the at least one correctiveaction responsive of its effectiveness.
 4. The computer-implementedmethod of claim 3, further comprising: storing in a data source at leastone of: the at least one behavioral pattern, the at least one correctiveaction, and the ranking of the at least one corrective action.
 5. Thecomputer-implemented method of claim 1, further comprising: predicting,based on the at least one behavioral pattern and the monitored sensoryinput data, a failure of the industrial machine; and selecting at leastone corrective action based on the industrial machine failureprediction.
 6. The computer-implemented method of claim 1, furthercomprising: preprocessing raw sensory input data received from thesensors; and storing the preprocessed raw sensory input data in a datasource.
 7. The computer-implemented method of claim 6, wherein thepreprocessing includes at least one of: data cleansing, normalization,rescaling, re-trending, reformatting, and noise filtering.
 8. Thecomputer-implemented method of claim 1, further comprising: determiningnormal machine behavioral patterns based on the sensory input data ofthe machine; and identifying, based on the normal machine behavioralpatterns, at least one machine behavioral pattern that is indicative ofthe at least a suboptimal operation of the machine.
 9. Thecomputer-implemented method of claim 1, further comprising: determininga maximum threshold and a minimum threshold based on at least onerepeated sequence from sensory input data; and detecting an anomalyindicative of the at least one suboptimal operation of the machine basedon the determined maximum and minimum thresholds.
 10. A non-transitorycomputer readable medium having stored thereon instructions for causinga processing circuitry to perform a process, the process comprising:monitoring sensory input data related to a machine; analyzing, usingunsupervised machine learning, the monitored sensory inputs, wherein theoutput of the unsupervised machine learning includes at least oneindicator; identifying, based on the at least one indicator, at leastone machine behavioral pattern that is indicative of at least onesuboptimal operation of the machine; selecting at least one correctiveaction based on the at least one machine behavioral pattern, wherein thesystem is further configured to: extract one or more characteristicsassociated with the at least one machine behavioral pattern that isindicative of the at least one suboptimal operation of the at least onemachine; search in at least one data source that comprises a pluralityof corrective actions for one or more corrective actions based onextracted characteristics; and, select at least one corrective actionhaving a first probability score that is above a first predeterminedthreshold to repair the at least one suboptimal operation of themachine, and a second probability score that is below a secondpredetermined threshold to damage the machine; and, performing the atleast one corrective action on the machine.
 11. A system for identifyingand repairing suboptimal operation of a machine, comprising: aprocessing circuitry; and a memory, the memory containing instructionsthat, when executed by the processing circuitry, configure the systemto: monitor sensory input data related to an industrial machine;analyze, using an unsupervised machine learning model, the monitoredsensory inputs, wherein the output of the unsupervised machine learningmodel includes at least one indicator; identify, based on the at leastone indicator, at least one behavioral pattern related to the industrialmachine, wherein each of the at least one behavioral pattern isindicative of at least one suboptimal operation of the industrialmachine; select at least one corrective action based on the at least onebehavioral pattern, wherein selecting at least one corrective actionfurther comprises: extracting one or more characteristics associatedwith the at least one machine behavioral pattern that is indicative ofthe at least one suboptimal operation of the at least one machine;searching in at least one data source that comprises a plurality ofcorrective actions for one or more corrective actions based on extractedcharacteristics; and, selecting at least one corrective action having afirst probability score that is above a first predetermined threshold torepair the at least one suboptimal operation of the machine, and asecond probability score that is below a second predetermined thresholdto damage the machine; and perform the at least one corrective action onthe industrial machine.
 12. The system of claim 11, wherein the systemis further configured to: monitor the sensory input data associated withthe industrial machine to determine an influence of the correctiveaction on the at least one machine.
 13. The system of claim 12, whereinthe system is further configured to: rank the at least one correctiveaction responsive of its effectiveness.
 14. The system of claim 13,wherein the system is further configured to: store in a data source atleast one of: the at least one behavioral pattern, the at least onecorrective action, and the ranking of the at least one correctiveaction.
 15. The system of claim 11, wherein the system is furtherconfigured to: predict, based on the at least one behavioral pattern andthe monitored sensory input data, a failure of the industrial machine;and select at least one corrective action based on the industrialmachine failure prediction.
 16. The system of claim 11, wherein thesystem is further configured to: preprocess raw sensory input datareceived from the sensors; and store the preprocessed raw sensory inputdata in a data source.
 17. The system of claim 16, wherein thepreprocessing includes at least one of: data cleansing, normalization,rescaling, re-trending, reformatting, and noise filtering.
 18. Thesystem of claim 11, wherein the system is further configured to:determine normal machine behavioral patterns based on the sensory inputdata of the machine; and identify, based on the normal machinebehavioral patterns, at least one machine behavioral pattern that isindicative of the at least a suboptimal operation of the machine. 19.The system of claim 11, wherein the system is further configured to:determine a maximum threshold and a minimum threshold based on at leastone repeated sequence from sensory input data; and detect an anomalyindicative of the at least one suboptimal operation of the machine basedon the determined maximum and minimum thresholds.